Remote sensing systems, such as RADAR systems used to detect the presence, position, speed and/or other characteristics of objects, are vital to both civilian and military operations. These systems utilize electromagnetic (EM) waves to detect and classify, for example, precipitation and natural/man-made objects. In operation, these systems typically transmit “beams” or signals toward targets, and process reflected return signals (or echoes) for target identification and characterization. The presence of clutter in these return signals creates a significant technical challenge in the accurate processing of these signals.
In general, clutter, or components of return signals which are not of interest, can be attributed to both stationary and moving characteristics of a given background scene. Relatively stationary clutter sources include, for example, the ground, sea and various atmospheric conditions. Moving or Doppler-varying clutter sources may include precipitation as well as generally stationary objects comprising moving components. One example of a particularly problematic and growing source of Doppler-varying clutter is that generated by the wide-spread installation of wind turbine power generation farms. The level of clutter created by these natural and man-made sources may be orders of magnitude stronger than return signals generated by desired targets (e.g. aircraft). This clutter decreases RADAR performance by hindering the system's ability to detect targets and/or increase the probability of a false target detection.
Several solutions have been implemented into RADAR signal processing systems in an effort to reduce clutter levels and improve system performance. For example, clutter mapping algorithms have been developed which create a background map and perform constant false alarm rate (CFAR) thresholding. More particularly, return signals may be received by an antenna, amplified, down-converted and passed through detector circuitry. These signals comprise desired return (e.g. target) data as well as components comprising unwanted power from clutter sources. CFAR processing attempts to determine a threshold power above which any return can be considered to originate from a target. This threshold is set typically to achieve a desired probability of a false alarm, or false alarm rate. As unwanted clutter and interference sources may have noise levels which change both spatially and temporally, a varying threshold may be used to maintain a generally constant probability of false alarm. However, in areas of high-clutter, and thus high power returns, targets, such as aircraft passing over or through the clutter, may not pass this threshold test consistently. Further still, Doppler nulling has been used to address the clutter problem. However, Doppler nulling tends to create Doppler blind regions, with residual “uncancelled” clutter addressed via clutter mapping.
Referring generally to FIG. 1, many RADAR systems, such as air traffic control (ATC) systems, operate by forming two or more beams depicted notionally as beams 12,14 oriented at differing elevation angles with respect to a reflector antenna 10. In the illustrated system, a lower beam 14 is provided for tracking objects with lower elevation and a higher beam 12 is provided for tracking objects with higher elevation. Often lower beam 14 is subject to more clutter than higher beam 12, as ground-level clutter is typically encountered more frequently. Beam selection techniques may be implemented into these systems to mitigate clutter signal data. In the case of high, or higher, average clutter received (or expected to be received) by lower beam 14, selection (e.g. processing) of only beam 12 will reduce the average clutter received. However, this reduction is achieved at the expense of system coverage.
Analog spatial nulling techniques have also been implemented into antenna systems to reduce interference and clutter. These techniques may include reducing the sensitivity of a receiver/antenna in the direction of an identified interfering signal using analog weights. For example, FIG. 2 shows an analog weighting system and method implemented at the RF level for reducing or cancelling unwanted signals, wherein spatial nulling is performed prior to signal demodulation and Doppler filtering. System 100 may be responsive to one or more input sources, for example, first and second beams 12,14 of FIG. 1. These signals are received by first and second channels of a front end module, including, for example, low-noise amplifiers 101. Spatial nulling may be performed on each signal channel by a set of weights embodied as vector modulators 102 generated from a processor 104. The resulting modulated signals may be combined by combiner 106 and output to a receiver 108. Subsequent processing (e.g. Doppler filtering) may be performed by a second processor 110. It should be noted that because signal weighting is performed at the analog RF level before further signal processing, the same analog weights are applied across all Doppler-frequencies for a given range/azimuth position. As currently implemented, these analog systems and methods have significant limitations.
Improved systems and methods for clutter reduction are desired.